SoroushVahidi/scidocs huggingface dataset:
Bayesian Grasp Planning
(2011)

We present a Bayesian framework for grasp planning that takes into account uncertainty in object shape or pose, as well as robot motion error. When trying to grasp objects based on noisy sensor data, a common problem is errors in perception, which cause the geometry or pose of the object to be uncertain. For each hypothesis about the geometry or pose of the object to be grasped, different sets of grasps can be planned. Of the resulting grasps, some are likely to work only if particular hypotheses are true, but some may work on most or even all hypotheses. Likewise, some grasps are easily broken by small errors in robot motion, but other grasps are robust to such errors. Our probabilistic framework takes into account all of these factors while trying to estimate the overall probability of success of each grasp, allowing us to select grasps that are robust to incorrect object recognition as well as motion error due to imperfect robot calibration. We demonstrate our framework while using the PR2 robot to grasp common household objects.
Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization.
(2008)

An iterative algorithm, based on recent work in compressive sensing, is developed for volume image reconstruction from a circular cone-beam scan. The algorithm minimizes the total variation (TV) of the image subject to the constraint that the estimated projection data is within a specified tolerance of the available data and that the values of the volume image are non-negative. The constraints are enforced by the use of projection onto convex sets (POCS) and the TV objective is minimized by steepest descent with an adaptive step-size. The algorithm is referred to as adaptive-steepest-descent-POCS (ASD-POCS). It appears to be robust against cone-beam artifacts, and may be particularly useful when the angular range is limited or when the angular sampling rate is low. The ASD-POCS algorithm is tested with the Defrise disk and jaw computerized phantoms. Some comparisons are performed with the POCS and expectation-maximization (EM) algorithms. Although the algorithm is presented in the context of circular cone-beam image reconstruction, it can also be applied to scanning geometries involving other x-ray source trajectories.
Iterative Adaptive Approaches to MIMO Radar Imaging
(2010)

Multiple-input multiple-output (MIMO) radar can achieve superior performance through waveform diversity over conventional phased-array radar systems. When a MIMO radar transmits orthogonal waveforms, the reflected signals from scatterers are linearly independent of each other. Therefore, adaptive receive filters, such as Capon and amplitude and phase estimation (APES) filters, can be directly employed in MIMO radar applications. High levels of noise and strong clutter, however, significantly worsen detection performance of the data-dependent beamformers due to a shortage of snapshots. The iterative adaptive approach (IAA), a nonparametric and user parameter-free weighted least-squares algorithm, was recently shown to offer improved resolution and interference rejection performance in several passive and active sensing applications. In this paper, we show how IAA can be extended to MIMO radar imaging, in both the negligible and nonnegligible intrapulse Doppler cases, and we also establish some theoretical convergence properties of IAA. In addition, we propose a regularized IAA algorithm, referred to as IAA-R, which can perform better than IAA by accounting for unrepresented additive noise terms in the signal model. Numerical examples are presented to demonstrate the superior performance of MIMO radar over single-input multiple-output (SIMO) radar, and further highlight the improved performance achieved with the proposed IAA-R method for target imaging.
You Cannot Sense My PINs: A Side-Channel Attack Deterrent Solution Based on Haptic Feedback on Touch-Enabled Devices
(2016)

In this paper, we introduce a novel and secure solution to mitigate side-channel attacks to capture the PINs like touchID and other credentials of touch-enabled devices. Our approach can protect haptic feedback enabled devices from potential direct observation techniques such as cameras and motion sense techniques including such as accelerometers in smart-watch. Both attacks use the concept of shoulder surfing in social engineering and were published recently (CCS'14 and CCS'15). Hand-held devices universally employ small vibration motors as an inexpensive way to provide haptic feedback. The strength of the haptic feedback depends on the brand and the device manufacturer. They are usually strong enough to produce sliding movement and make audible noises if the device is resting on the top of a desk when the vibration motor turns. However, when the device is held in the hand the vibration can only be sensed by the holder; it is usually impossible or uncertain for an observer to know when the vibration motor turns. Our proposed solution uses the haptic feedback to inform the internal state of the keypad to the user and takes advantage of the fact that the effect of haptic feedback can be easily cloaked in such a way that direct observation techniques and indirect sensing techniques will fail. We develop an application on Android cell phones to demonstrate it and invite users to test the code. Moreover, we use real smart-watch to sense the vibration of Android cell phones. Our experimental results show that our approach can mitigate the probability of sensing a 4-digit or 6-digit PINs using smart-watch to below practical value. Our approach also can mitigate the probability of recognizing a 4-digit or 6-digit PINs using a camera within 1 meter to below practical value because the user does not need to move his or her hand during the internal states to input different PINs.
Rapid word learning under uncertainty via cross-situational statistics.
(2007)

There are an infinite number of possible word-to-word pairings in naturalistic learning environments. Previous proposals to solve this mapping problem have focused on linguistic, social, representational, and attentional constraints at a single moment. This article discusses a cross-situational learning strategy based on computing distributional statistics across words, across referents, and, most important, across the co-occurrences of words and referents at multiple moments. We briefly exposed adults to a set of trials that each contained multiple spoken words and multiple pictures of individual objects; no information about word-picture correspondences was given within a trial. Nonetheless, over trials, subjects learned the word-picture mappings through cross-trial statistical relations. Different learning conditions varied the degree of within-trial reference uncertainty, the number of trials, and the length of trials. Overall, the remarkable performance of learners in various learning conditions suggests that they calculate cross-trial statistics with sufficient fidelity and by doing so rapidly learn word-referent pairs even in highly ambiguous learning contexts.
Word Segmentation : The Role of Distributional Cues
(1996)

One of the infant’s first tasks in language acquisition is to discover the words embedded in a mostly continuous speech stream. This learning problem might be solved by using distributional cues to word boundaries—for example, by computing the transitional probabilities between sounds in the language input and using the relative strengths of these probabilities to hypothesize word boundaries. The learner might be further aided by language-specific prosodic cues correlated with word boundaries. As a first step in testing these hypotheses, we briefly exposed adults to an artificial language in which the only cues available for word segmentation were the transitional probabilities between syllables. Subjects were able to learn the words of this language. Furthermore, the addition of certain prosodic cues served to enhance performance. These results suggest that distributional cues may play an important role in the initial word segmentation of language learners. q 1996 Academic Press, Inc.
Statistical learning by 8-month-old infants.
(1996)

Learners rely on a combination of experience-independent and experience-dependent mechanisms to extract information from the environment. Language acquisition involves both types of mechanisms, but most theorists emphasize the relative importance of experience-independent mechanisms. The present study shows that a fundamental task of language acquisition, segmentation of words from fluent speech, can be accomplished by 8-month-old infants based solely on the statistical relationships between neighboring speech sounds. Moreover, this word segmentation was based on statistical learning from only 2 minutes of exposure, suggesting that infants have access to a powerful mechanism for the computation of statistical properties of the language input.
A computational study of cross-situational techniques for learning word-to-meaning mappings
(1996)

This paper presents a computational study of part of the lexical-acquisition task faced by children, namely the acquisition of word-to-meaning mappings. It first approximates this task as a formal mathematical problem. It then presents an implemented algorithm for solving this problem, illustrating its operation on a small example. This algorithm offers one precise interpretation of the intuitive notions of cross-situational learning and the principle of contrast applied between words in an utterance. It robustly learns a homonymous lexicon despite noisy multi-word input, in the presence of referential uncertainty, with no prior knowledge that is specific to the language being learned. Computational simulations demonstrate the robustness of this algorithm and illustrate how algorithms based on cross-situational learning and the principle of contrast might be able to solve lexical-acquisition problems of the size faced by children, under weak, worst-case assumptions about the type and quantity of data available.
Substrate Integrated Waveguide Cavity-Backed Self-Triplexing Slot Antenna
(2017)

In this letter, a novel substrate integrated waveguide (SIW) cavity-backed self-triplexing slot antenna is proposed for triple-band communication. It is realized on a single-layer printed circuit board. The proposed antenna consists of a pair of bow-tie slots etched on an SIW cavity, and three different resonant modes are excited by two microstrip lines and a coaxial probe to operate at three distinct frequencies (7.89, 9.44, and 9.87 GHz). The band around 9.48 GHz is obtained due to radiation from one bow-tie slot fed by adjacent microstrip feedline and band around 9.87 GHz is due to radiation from other bow-tie slot fed by its adjacent microstrip feedline. On the other hand, lower band around 7.89 GHz is achieved because of radiation from both bow-tie slots excited by coaxial probe feedline. The measured isolation between any two feedlines is better than 22.5 dB. The measured realized gain is more than 7.2 dB at all the bands. Cross polarization below 36.5, 29.3, and 24.45 dB in broad sight direction and high front-to-back ratio of more than 17.3 dB at each resonant frequency are obtained.
Structural Health Monitoring Framework Based on Internet of Things: A Survey
(2017)

Internet of Things (IoT) has recently received a great attention due to its potential and capacity to be integrated into any complex system. As a result of rapid development of sensing technologies such as radio-frequency identification, sensors and the convergence of information technologies such as wireless communication and Internet, IoT is emerging as an important technology for monitoring systems. This paper reviews and introduces a framework for structural health monitoring (SHM) using IoT technologies on intelligent and reliable monitoring. Specifically, technologies involved in IoT and SHM system implementation as well as data routing strategy in IoT environment are presented. As the amount of data generated by sensing devices are voluminous and faster than ever, big data solutions are introduced to deal with the complex and large amount of data collected from sensors installed on structures.
Comparative Analysis of Intelligent Transportation Systems for Sustainable Environment in Smart Cities
(2017)

In recent works on the Internet of Vehicles (IoV), “intelligent” and “sustainable” have been the buzzwords in the context of transportation. Maintaining sustainability in IoV is always a challenge. Sustainability in IoV can be achieved not only by the use of pollution-free vehicular systems, but also by maintenance of road traffic safety or prevention of accidents or collisions. With the aim of establishing an effective sustainable transportation planning system, this study performs a short analysis of existing sustainable transportation methods in the IoV. This study also analyzes various characteristics of sustainability and the advantages and disadvantages of existing transportation systems. Toward the end, this study provides a clear suggestion for effective sustainable transportation planning aimed at the maintenance of an eco-friendly environment and road traffic safety, which, in turn, would lead to a sustainable transportation system.
An Improved DC-Link Voltage Fast Control Scheme for a PWM Rectifier-Inverter System
(2012)

This paper presents an improved dc-link voltage fast control strategy based on energy balance for a pulse width modulation (PWM) rectifier-inverter system to reduce the fluctuation of dc-link voltage. A conclusion is drawn that the energy in dc-link capacitor cannot be kept constant in dynamic process when the system operates in rectifier mode, even in ideal case. Meanwhile, the minimum dc-link voltage deviation is analyzed. Accordingly, a predictive dc-link voltage control scheme based on energy balance is proposed, while the grid current is regulated by deadbeat predictive control. A prediction method for output power is also introduced. Furthermore, a small-signal model with the control delay is adopted to analyze the stability and robustness of the control strategy. The simulation and experimental results demonstrate both good dynamic and steady-state performances of the rectifier-inverter system with the proposed control scheme.
Comparative Study on Load Balancing Techniques in Cloud Computing
(2014)

The present era has witnessed tremendous growth of the internet and various applications that are running over it. Cloud computing is the internet based technology, emphasizing its utility and follows pay-as-you-go model, hence became so popular with high demanding features. Load balancing is one of the interesting and prominent research topics in cloud computing, which has gained a large attention recently. Users are demanding more services with better results. Many algorithms and approaches are proposed by various researchers throughout the world, with the aim of balancing the overall workload among given nodes, while attaining the maximum throughput and minimum time. In this paper, various proposed algorithms addressing the issue of load balancing in Cloud Computing are analyzed and compared to provide a gist of the latest approaches in this research area.
Application of spiking neural networks and the bees algorithm to control chart pattern recognition
(2007)

Statistical process control (SPC) is a method for improving the quality o f products. Control charting plays a most important role in SPC. SPC control charts arc used for monitoring and detecting unnatural process behaviour. Unnatural patterns in control charts indicate unnatural causes for variations. Control chart pattern recognition is therefore important in SPC. Past research shows that although certain types o f charts, such as the CUSUM chart, might have powerful detection ability, they lack robustness and do not function automatically. In recent years, neural network techniques have been applied to automatic pattern recognition. Spiking Neural Networks (SNNs) belong to the third generation o f artificial neural networks, with spiking neurons as processing elements. In SNNs, time is an important feature for information representation and processing. This thesis proposes the application o f SNN techniques to control chart pattern recognition. It is designed to present an analysis o f the existing learning algorithms o f SNN for pattern recognition and to explain how and why spiking neurons have more computational power in comparison to the previous generation o f neural networks. This thesis focuses on the architecture and the learning procedure o f the network. Four new learning algorithms are presented with their specific architecture: Spiking Learning Vector Quantisation (S-LVQ), Enhanced-Spiking Learning Vector Quantisation (NS-LVQ), S-LVQ with Bees and NS-LVQ with Bees. The latter two algorithms employ a new intelligent swarm-based optimisation called the Bees Algorithm to optimise the LVQ pattern recognition networks. Overall, the aim o f the research is to develop a simple architecture for the proposed network as well as to develop a network that is efficient for application to control chart pattern recognition. Experiments show that the proposed architecture and the learning procedure give high pattern recognition accuracies.
On Different Facets of Regularization Theory
(2002)

This review provides a comprehensive understanding of regularization theory from different perspectives, emphasizing smoothness and simplicity principles. Using the tools of operator theory and Fourier analysis, it is shown that the solution of the classical Tikhonov regularization problem can be derived from the regularized functional defined by a linear differential (integral) operator in the spatial (Fourier) domain. State-ofthe-art research relevant to the regularization theory is reviewed, covering Occam's razor, minimum length description, Bayesian theory, pruning algorithms, informational (entropy) theory, statistical learning theory, and equivalent regularization. The universal principle of regularization in terms of Kolmogorov complexity is discussed. Finally, some prospective studies on regularization theory and beyond are suggested.
Never-Ending Multiword Expressions Learning
(2015)

This paper introduces NEMWEL, a system that performs Never-Ending MultiWord Expressions Learning. Instead of using a static corpus and classifier, NEMWEL applies supervised learning on automatically crawled news texts. Moreover, it uses its own results to periodically retrain the classifier, bootstrapping on its own results. In addition to a detailed description of the system’s architecture and its modules, we report the results of a manual evaluation. It shows that NEMWEL is capable of learning new expressions over time with improved precision.
Text Clustering for Topic Detection
(2004)

The world wide web represents vast stores of information. However, the sheer amount of such information makes it practically impossible for any human user to be aware of much of it. Therefore, it would be very helpful to have a system that automatically discovers relevant, yet previously unknown information, and reports it to users in human-readable form. As the first attempt to accomplish such a goal, we proposed a new clustering algorithm and compared it with existing clustering algorithms. The proposed method is motivated by constructive and competitive learning from neural network research. In the construction phase, it tries to find the optimal number of clusters by adding a new cluster when the intrinsic difference between the instance presented and the existing clusters is detected. Each cluster then moves toward the optimal cluster center according to the learning rate by adjusting its weight vector. From the experimental results on the three different real world data sets, the proposed method shows an even trend of performance across the different domains, while the performance of our algorithm on text domains was better than that reported in previous research.
Direct Desktop Printed-Circuits-on-Paper Flexible Electronics
(2013)

There currently lacks of a way to directly write out electronics, just like printing pictures on paper by an office printer. Here we show a desktop printing of flexible circuits on paper via developing liquid metal ink and related working mechanisms. Through modifying adhesion of the ink, overcoming its high surface tension by dispensing machine and designing a brush like porous pinhead for printing alloy and identifying matched substrate materials among different papers, the slightly oxidized alloy ink was demonstrated to be flexibly printed on coated paper, which could compose various functional electronics and the concept of Printed-Circuits-on-Paper was thus presented. Further, RTV silicone rubber was adopted as isolating inks and packaging material to guarantee the functional stability of the circuit, which suggests an approach for printing 3D hybrid electro-mechanical device. The present work paved the way for a low cost and easygoing method in directly printing paper electronics.
A study of large-scale ethnicity estimation with gender and age variations
(2010)

In this paper we study large-scale ethnicity estimation under variations of age and gender. The biologically-inspired features are applied to ethnicity classification for the first time. Through a large number of experiments on a large database with more than 21,000 face images, we systematically study the effect of gender and age variations on ethnicity estimation. Our finding is that ethnicity classification can have high accuracy in most cases, but an interesting phenomenon is observed that the ethnic classification accuracies could be reduced by 6∼8% in average when female faces are used for training while males for testing. The study results provide a guide for face processing on a multi-ethnic database, e.g., image collection from the Internet, and may inspire further psychological studies on ethnic grouping with gender and age variations. We also apply the methods to the whole MORPH-II database with more than 55,000 face images for ethnicity classification of five races. It is the first time that ethnicity estimation is performed on so large a database.
Engineering for Predictive Modeling using Reinforcement Learning
(2017)

Feature engineering is a crucial step in the process of predictive modeling. It involves the transformation of given feature space, typically using mathematical functions, with the objective of reducing the modeling error for a given target. However, there is no well-defined basis for performing effective feature engineering. It involves domain knowledge, intuition, and most of all, a lengthy process of trial and error. The human attention involved in overseeing this process significantly influences the cost of model generation. We present a new framework to automate feature engineering. It is based on performance driven exploration of a transformation graph, which systematically and compactly enumerates the space of given options. A highly efficient exploration strategy is derived through reinforcement learning on past examples.